What are other examples of theoretical machine learning books?
Pattern Recognition And Machine Learning is a great theoretical book. I don't know anything better on standard ML. I read several pages from it myself and all my colleagues researchers suggest to look there if you are not sure about some concepts. The 2 problems with it are that it's huge and it doesn't cover almost all deep learning models known for today.
So, in addition, I'd suggest you look at Deep Learning by Ian Goodfellow et al.
Your concerns about not studying topology, measure theory and group theory are groundless. These sections of math aren't prerequisites in any way, they aren't even discussed anywhere I know.
Actually, ML theory is more like probability theory and statistics. Especially, statistical learning theory (which is nothing more than probability theory and statistics). I haven't read any books on SLT so have a look at this answer.